There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality.

Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

I and i

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, particularly related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges. Photonics offers an attractive platform for implementing neuromorphic computing due to its low latency, multiplexing capabilities and integrated on-chip technology.

Photonics for artificial intelligence and neuromorphic computing

Research in photonic computing has flourished due to the proliferation of optoelectronic components on photonic integration platforms. Photonic integrated circuits have enabled ultrafast artificial neural networks, providing a framework for a new class of information processing machines. Algorithms running on such hardware have the potential to address the growing demand for machine learning and artificial intelligence, in areas such as medical diagnosis, telecommunications, and high-performance and scientific computing. In parallel, the development of neuromorphic electronics has highlighted challenges in that domain, in particular, related to processor latency. Neuromorphic photonics offers sub-nanosecond latencies, providing a complementary opportunity to extend the domain of artificial intelligence. Here, we review recent advances in integrated photonic neuromorphic systems, discuss current and future challenges, and outline the advances in science and technology needed to meet those challenges.

Frontiers in neuroscience

Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.

/pdf/an-optical-neural-chip-for-implementing-complex-valued-25pptq8q6o.pdf

An optical neural chip for implementing complex-valued neural network.

Photonic neuromorphic computing is raising a growing interest as it promises to provide massive parallelism and low power consumption. In this paper, we demonstrate for the first time a feed-forward neural network via an 8 × 8 Indium Phosphide cross-connect chip, where up to 8 on-chip weighted addition circuits are co-integrated, based on semiconductor optical amplifier technology. We perform the weight calibration per neuron, resulting in a normalized root mean square error smaller than 0.08 and a best case dynamic range of 27 dB. The 4 input to 1 output weighted addition operation is executed on-chip and is part of a neuron, whose non-linear function is implemented via software. A three feedback loop optimization procedure is demonstrated to enable an output neuron accuracy improvement of up to 55%. The exploitation of this technology as neural network is evaluated by implementing a trained 3-layer photonic deep neural network to solve the Iris flower classification problem. Prediction accuracy of 85.8% is achieved, with respect to the 95% accuracy obtained via a computer. A comprehensive analysis of the error evolution in our system reveals that the electrical/optical conversions dominate the error contribution, which suggests that an all optical approach is preferable for future neuromorphic computing hardware design.

Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect

The computing industry is rapidly moving from a programming to a learning area, with the reign of the von Neumann architecture starting to fade, after many years of dominance. The new computing paradigms of non-von Neumann architectures have started leading to the development of emerging artificial neural network (ANN)-based analog electronic artificial intelligence (AI) chipsets with remarkable energy efficiency. However, the size and energy advantages of electronic processing elements are naturally counteracted by the speed and power limits of the electronic interconnects inside the circuits due to resistor-capacitor (RC) parasitic effects. Neuromorphic photonics has come forward as a new research field, which aims to transfer the well-known high-bandwidth and low-energy interconnect credentials of photonic circuitry in the area of neuromorphic platforms. The high potential of neuromorphic photonics and their well-established promise for fJ/Multiply-ACcumulate energy efficiencies at orders of magnitudes higher neuron densities require a number of breakthroughs along the entire technology stack, being confronted with a major advancement in the selection of the best-in-class photonic material platforms for weighting and activation functions and their transformation into co-integrated photonic computational engines. With this paper, we analyze the current status in neuromorphic computing and in available photonic integrated technologies and propose a novel three-dimensional computational unit which, with its compactness, ultrahigh efficiency, and lossless interconnectivity, is foreseen to allow scalable computation AI chipsets that outperform electronics in computational speed and energy efficiency to shape the future of neuromorphic computing.

Neuromorphic Photonics: 2D or not 2D?

We demonstrate a monolithically integrated SOA-based photonic neuron, including both the weighted addition and a wavelength converter with tunable laser as nonlinear function, allowing for lossless computation of 8 Giga operation/s with an 89% accuracy.

Lossless Monolithically Integrated Photonic InP Neuron for All-Optical Computation

We demonstrate high fidelity weighted addition of four 10 Gb/s on-off keyed data channels on a SOA-based monolithically integrated cross-connect, with a reading error dispersion below 0.2 with respect to the target. These results open the route to feasible 8x8 one-layer neuron interconnectivity for photonic integrated deep neural networks.

WDM Weighted Sum in an 8x8 SOA-Based InP Cross-Connect for Photonic Deep Neural Networks

We demonstrate for the first time Iris flowers classification by implementing a trained 3-layer neural network with an SOA-based InP cross-connect chip. Classification accuracy of 85.8% is achieved, 9.2% lower than what obtained via a computer.

Bin Shi

Papers

Deep Neural Network Through an InP SOA-Based Photonic Integrated Cross-Connect

Neuromorphic Photonics: 2D or not 2D?

Lossless Monolithically Integrated Photonic InP Neuron for All-Optical Computation

WDM Weighted Sum in an 8x8 SOA-Based InP Cross-Connect for Photonic Deep Neural Networks

Image Classification with a 3-Layer SOA-Based Photonic Integrated Neural Network